For instance, entity number 10101 (European Union) was the most frequently mentioned entity in German language news in 2010 (before Angela Merkel) and it was the second most frequently mentioned name in English language news (after Barack Obama).

for look-up purposes in most European languages, it does not matter whether Silvio Berlusconi is an Italian, German or Romanian name, as long as it gets recognised in texts of that language.


While many name variants will only occur in some languages and not in others, it does not normally do any harm to search for the foreign language variants in a text.

in some cases, a name variant may have a different meaning in other languages.


This first release of JRC-Names does not recognise morphologically inflected variants of entity names.

it is planned that future versions will include morphological variant recognition in one of two ways.


During the last decade, several named entity (NE) annotated corpora were built, thanks to a large series of evaluation campaigns (Fort et al., 2009).

most of these gold-standard data are available only for English or for a few languages.


This means that the translation of a source word can be found by identifying a target word which has the most similar context vector in a comparable corpus.

a direct comparison of vectors in two different languages is not possible, which is why a dictionary is needed to first translate the features of source context vectors into the target language and compute similarity on those.


Experimenting with a context similarity threshold as well as a minimum frequency criterion for identical words did not improve the results.

we use context-dependent cognates because calculat-ing cognates between all lemmata of specific parts of speech proved to be very noisy even on high cognate thresholds and it did not have a positive impact on this task.


For example, "For a very long time I've had a problem with feeling really awful when I try to get up in the morning" is a description of some symptoms and should not be annotated as subjective.

"I don't know if that makes sense, it seems to me that the new drug which stimulates red blood cell production would be a more logical approach, erythropoiten (sp?)"


Such files contained rather hot discussions (e.g., "Candidayeast Bloom"," MSG sensitivity", "Homeopathy").

some files contained newsletters, conference announcements, other announcements that were considered objective (Section 2); these files were deleted from annotation.


For adjectives and verbs, requiring a least common subsumer always yields related pairs.

the upper levels of the noun hierarchy are very general, and we ensure that only related pairs are used by ignoring the upper three levels of the noun hierarchy for this feature.


Once identified as such, they can be treated similarly to single words in syntactic parsing for example.

the meaning of their parts and their connection alone cannot determine the semantics of the whole phrase, which yields that higher level applications need to pay special attention to them.


It is this similarity that we would like to exploit when investigating the effect of already known NEs/noun compounds on the identification of the other type.

our research focuses on the role of noun compounds and named entities in keyphrase extraction.


Results can be seen in CRF row of Table 2.

the database included many sentences without any labeled noun compounds hence negative examples were overrepresented.


First, the Stanford NER model was used for identifying NEs.

we assumed that a 1 Linguistic Data Consortium (LDC), catalogId: LDC2003T05 model trained on Wikipedia could more effectively identify NEs in Wikipedia (as it is the same domain).


Attorney General): some of the dissimilarities between the manual annotations were related to this problem.

we assumed that a term can occur either as a NE or a noun compound.


We show that inclusion of some genre-specific training material is necessary, especially when less generalizable genres are to be labeled.

most effect is perceived by adding more data to training.


that may be related to the topic of the document.

the identification of concepts involves semantic analysis, and therefore, we can identify synonyms or other types of semantic relationships.


For instance, the words harassment and molestation represent the same concepts (since they both belong to the same synset in WordNet), so they are grouped together in this approach, whereas in the previous one, where only the frequency of terms is taken into consideration, they are considered two distinct words.

the drawback of this approach is that such kind of resources may not be available for all languages, and therefore we might have problems in applying this approach.


Analogously, the same happens for the Germanic languages (English and German).

the highest differences between languages can be found in the number of NE and concepts detected.


This may happens because these languages have a lot of specific resources for dealing with them.

the performance for French and German linguistic resources may not be as accurate as for the other languages, thus affecting the results.


In order to predict θ new , different optimization algorithms such as conjugate gradient can be used.


Therefore, it is very hard for a learning algorithm to precisely determine the relation types.

results of table 4, which ignores NONE relations, are satisfactory.


Tables 1 and 2 show that the proposed lexicalized features yielded an important gain in  In isolation, these features result in similar improvements of approximately 4% in terms of Fmeasure, whereas together they complement each other yielding about 12% improvement on the outof-domain dataset.

disappointingly our system performs worse than that by Mitsumori et al.


Similar research projects have already developed procedures using standard WordNet-based similarities (Corley and Mihalcea, 2005;Hickl and Bensley, 2007).

in our case we also consider string-based similarities for the final similarity score.


In this table we can see that there is a 5.77% increase in accuracy if we apply a language detector using the first 1, 000 words of a document.

given that we use a two-pass algorithm, the number of FPs would be 0, which means that the final accuracy after applying a language detection software would be 0.9984, which is a 5.87% higher than the baseline.


Therefore, in this case we would have to use our baseline system which is mainly based on lexical measures.

in the second user scenario, where we only have one suspicious document to analyze, the application of the aforementioned advanced techniques is suitable given the smaller amount of data to process.


Indeed, many Romanian terms were borrowed from French and these terms present high orthographic similarities.

table 4 Evaluation of the 4-grams method before and after orthographic adjustments step our method extracts some ambiguous candidates such as numéro 'number' -nume 'name', compléter 'complete' -compune 'compose'.


Recasens and Hovy (2010) explore the impact on coreference resolution performance by varying several prominent contextual factors; they measure performance across corpora, languages, annotation schemes and preprocessing.

their set of languages consisted of English and Spanish only.


(Luo et al., 2004;Denis and Baldridge, 2007)).

in many approaches to coreference resolution, the problem is even more restricted, and the coreference resolution component expects only such mentions that are coreferent in the present context, i.e.


This example shows that with the SEMEVAL scorer, all metrics but MUC, are sensitive to singletons in the system output and in the gold standard data.

the presence of a singleton (Y) in the system output leads to a decrease in the results while an additional singleton (X) in the gold standard increases results although the system output is unchanged.


Especially in a setting in which both the gold standard and the system output contain singletons, the evaluation scores of both versions of the scorer are artificially boosted.

the presence of singletons in the system output also has an effect on evaluation, but to a considerably lesser degree.


Entity Instantiations are not considered in the MUC and ACE annotation schemes, which consider relationships between different types of entity, such as those between persons and locations, rather than our groups and instances of entities of the same type.

the algorithms used to classify these semantic relationship might still be applicable to our problem.


analyzing the scope of modalities or negations).

these questions are not directly addressed by most existing annotation schemes, especially the most popular ones.


The cases of disagreement are rare and affect quite specific sentences (with negation or with a complex structure); they can all be solved after discussion between domain experts.

this scheme does not cover all possible cases and should be extended for specific needs.


The final Persian WordNet is obtained by remo ing links those probabilities are less than a thr shold or by selecting the top probable links as links.

the result of this method is dependent to the corpus that is used in E In fact, the probability of selecting correct cand date synsets of a given Persian word that haven't appeared in corpus will be zero and these synsets We guess that better results can be obtained by using more effective methods to initialize the parameter values rather than using FarsNet which may initialize some senses with higher values even if they have not even been observed , the mean average precision (MAP) over different recall rates with respect to diffe ent POS tags is shown.


The final Persian WordNet is obtained by removing links those probabilities are less than a threshold or by selecting the top probable links as links.

the result of this method is dependent to the corpus that is used in E-step.


Many real world problems that require authorship attribution, such as forensics (e.g., Luyckx and Daelemans (2008)) or authorship dispute for old literature (e.g., Mosteller and Wallace (1984)) may not have in-domain training data readily available.

most previous work to date has focused on authorship attribution only for in-domain text (e.g., Stamatatos et al.


As can be seen in Table 1, the categories of LCSH are more finegrained, and some of the categories are overlapping eg:"history" and "history and criticism".

the categories of LCC are more coarsegrained so that they are more distinctive from each other.


For example, systems using WordNet (Fellbaum, 1998) had sizable performance gains for SemEval-1 Task 4.

this advantage was mainly due to manually annotated WordNet senses for the relation arguments being provided for this task.


2 The most relevant related publication is that of O Séaghdha and Copestake (2009), who combine attributional and relational features using kernels.

they are interested in a special kind of relations: between the nouns in a noun-noun compounds like steel knife.


Semantic argument structures are often incomplete in that core arguments are not locally instantiated.

many of these implicit arguments can be linked to referents in the wider context.


Semantic Role Labeling (SRL) is traditionally concerned with identifying the overtly realized arguments of a predicate.

in a natural discourse only a relatively small proportion of the theoretically possible semantic arguments tend to be locally instantiated in the same clause or sentence that contains the target predicate.


5 In theory, we could use customized strategies for antecedent finding depending, for instance, on whether the null instantiation is licensed by a construction or by a lexical item, or depending on the identity of the null-instantiated frame element.

at the moment we treat the problem of antecedent finding in the same way for all null-instantiated frame elements.


Font-Llitjós (2007) already anticipated the advisability of incorporating an active learning mechanism in her transfer rule refinement system, asking the user to validate different translations deduced from the initial hypothesis.

this active learning approach has not yet been undertaken.


As a further motivation for our choice we note that the method of C&VN can be extended to deal with wrong and incomplete lexical descriptions of words which are already in the lexicon (Cholakov and van Noord, 2010b).

this extension is beyond the scope of the current paper.


For instance, the TreeTagger POS tagger (Schmid, 1994) could provide both POS and stem information with high accuracy.

the generation of the paradigms allows us to extract contexts in which other forms of a given unknown word occur and thus, we have access to much more and linguistically diverse data.


The latter has incorporated valence features to PCFGs and lexicons and observes slight improvements on performances.

lexical resources that contain valence features were obtained automatically from a corpus.


This comes most probably from the grammar which is too fragmented because of the significant size of the part-of-speech tagset (as shown in table 5).

the effect is reversed as soon as we use levels of the hierarchy of tables (levels 2 and 3 only).


The Pair HMMs again achieve a better F-score value compared to the best shared task result.

the CONs2 model has a relatively poor performance than it did for English-Hindi above.


context-dependent DBN model achieves a slightly better F-score than the Pair HMMs for the first 200 suggestions of transliteration pairs using the models.

we found out that an increase in recall resulted in a faster drop of precision for the context-dependent DBN model compared to the drop for the Pair HMMs.


[Negative] which are specific to the domain: generally "easy" can not be considered as a positive word ("It is easy to lose money" has a negative connotation).

the association [easy + to + verb indicating a functional characteristic of the printer] expresses a positive opinion.


For verbs taking abstract arguments without definitive visual features, the classifier can often learn to disregard the visual data.

for verbs taking physical arguments (such as food, animals, or people), the classifier can make accurate predictions using the nouns' visual properties.


For the purpose of annotation, the use of XML tags appears to be appropriate, since it allows us to describe trees.

taking into account the full hierarchical structure represents an additional difficulty for the automatic clustering procedure; in this first approach, the problem is treated as linear, i.e.


Figure 2 shows that Jaccard index increases when the number of groups decreases until there is only one group.

the maximum of Jaccard index appears around 8 groups as does the second maximum of the corrected Rand index.


On the one hand, the Jaccard index takes a non-zero value in presence of single group, an artefact due to the absence of correction for self-similarity in (2).

the corrected Rand index can take negative values, which means that results are worse than chance.


As for the corrected Rand, it shows that weighted K-means improves the clustering.

a certain number of regularities transpire.


There is some evidence in the research literature to suggest that the element of surprise can move markets (Bomfim and N., 2000).


we know that "biased" people are likely to have loyalties to companies or organizations, whereas "unbiased" people are usually independent because they are employed by companies who provide impartial advice to client.


They aim to develop tools capable of assessing the difficulty of texts for a given population through textual features only (such as the number of letters per word, the number of words per sentence, etc.).

while many studies have examined the readability of English L1 (Chall and Dale, 1995), there are far fewer studies on readability in an L2, especially in French as a foreign language (FFL).


In most cases, formulas for native speakers have been applied to L2 texts.

the validity of such an approach is far from established, because it relies on three suspect assumptions : (1) the understanding of readers in the L2 is comparable to that of native speakers, (2) the textual features considered in L1 formulas are relevant to L2 reading, and (3) the weighting of these variables may be the same in a formula for L1 and L2.


A common method to estimate to what extent a MWE is known in a given population is to use its objective frequency.

the hypothesis that MWEs that are less frequent in the language may be more difficult to read has hardly been explored in readability.


The CEFR normally has six levels -A1 (Breakthrough); A2 (Waystage); B1 (Threshold); B2 (Vantage); C1 (Effective Operational Proficiency) and C2 (Mastery).

to better reflect the evolution of learners, which is faster in the early stages of learning, we split the first three levels into two, thereby obtaining a total of nine levels.


Faced with these results, one might conclude that MWEs are not as good predictors as the simple complex nominal structures (θ = 0).

it seems more accurate to limit this deficiency to MWEs that are detected automatically using statistical techniques.


As described in more detail in Section 2, these transformations do not necessarily capture the syntactic structure of entailment-preserving transformations.

a rich inventory of knowledge-based operations was employed by Bar-Haim et al.


DIRT (Lin and Pantel, 2001), would be utilized naturally by substitution of a complete sub-tree by another, which cannot be performed by the above tree-edit-distance operations.

a set of linguistically-motivated operations was proposed independently by Harmeling (2009) and by Bar-Haim et al.


For each (T,H) pair there might be many proofs.

for positive pairs, we assume there exists a "correct" proof, i.e.


A reasonable explanation for this phenomenon is that the system learned that some operations are less reliable than other operations, and tried to avoid them whenever possible.

these operations could not be avoided in negative pairs, resulting in higher feature values.


Note that the structural alignment information allows us to correctly classify plate and container, and stop and button, as equivalent, although they are not particularly similar in WordNet.

especially for rare terms, our algorithm seems too strict: it did not combine the three power setting PDSs.


it is natural to first include the identification of the item as a member of a generic class, then to describe the object's constituency or attributes followed by a specific example and so on.

a comparison of two objects should use another combination to be effective and coherent.


evidence, we have used the SPADE discourse parser (Soricut and Marcu, 2003).

in order to identify predicates within a single proposition -e.g.


Though sentence 1 was classified as containing a comparison predicate, it did not fulfil the semantic constraint (shown in Figure 4) that the topic of the sentence (Carmax) be the focus of the sentence 5 .

5 To identify this, we test if the subject or object of the sentence 8 was not included, because it did not contain any of the rhetorical predicate which can fill the slots of this schema.


Navarro and Mäkinen (2007), make it possible to identify the match range for P in O(|P |) time 4 , and since no tokenization is required, recombining matches for individual tokens as in the case of inverted files is unnecessary.

it is impossible to store annotationrelated information in the suffix array.


For example, standard information retrieval usually considers only the content dimension (keyword overlap between query and document).

a scholar in digital humanities might be interested in texts that are similar to a reference document with respect to style and structure, while texts with similar content are of minor interest.


They contain text pairs together with human judgments about their perceived similarity.

none of those datasets has yet undergone a thorough analysis with respect to the dimensions of text similarity encoded therein.


Like other Romance languages, Romanian has traditionally received a Latin-inspired classification of verbs into 4 conjugational classes (or sometimes 5, where the 4th conjugation is divided between verbs with the infinitive ending in i andî, respectively) based on the ending of their infinitival form alone (Costanzo, 2011).

this infinitive-based classification has often been found inadequate due to the many conjugational patterns that have been found in each class and to its inability to account for the behavior of partially irregular verbs (those whose stem has a smaller number of allomorphs than the completely irregular such as "a fi") during their conjugation.


The power of this grammar does not lie in its language: some of its derivations are general enough to accept any string over the alphabet ending in the letter t, for example.

derivations in this grammar represent a generative process that can build present indicative forms of verbs.


We should also mention that there are few works that proposed a modeling of NEs for explicitly representing the effects of meaning within the NE and explaining phenomena like synecdoche and the metonymy (Poibeau, 2005).

these works don't treat the concept of embedded NEs which is very important and can help to implement the recognition and the translation process of NEs.


It is obvious that if a NE contains several NEs, it can cause different problems such as polysemy as mentioned in (Poibeau, 2005).

it proves also the concept of embedded NEs.


It doesn't satisfy the saturation principle.

it should be noted that this word can contain a NE.


For instance, on the one hand, if a NE in the source language contains an adjective then we have to know whether this adjective belongs to the trigger word or to the noun that comes just before.

if a NE in the source language contains a noun then some rules are applied to solve the problem of contracted forms in Arabic.


our method differs from others in several ways.


This method is simple, fast, and uses less memory.

with other methods, our feature represents not one semantic argument but all semantic arguments of a verb.


Many researchers have investigated text classification and the naive Bayes (NB) is one of the most famous methods for it.

when we use the NB classifier to categorize the products, the accuracies were not very high, especially when the data distribution is very skewed.


Therefore when the NB classifier and the CNB classifier were compared, it is still unansuwered question that which is the better method to classify these product pages.

the experiments show that our proposal method, the NNB is always the best method for the classification of the product pages of the three genres.


Crowdsourcing is an effective and cost-efficient way to collect human corrected (HC) sentences.

before feeding back the crowdsourcing data to MT system, there are two challenges (a) how to measure the quality of HC sentences and (b) how to select good quality HC sentences for enhancing the translation models.


Above features treat each translation set as a whole, and user information are ignored.

we believe that the user information can also be predictable.


number of words 6 ) between the candidate concepts: in the training corpus there is never more than 65 words between two related concepts.

two concepts which are not in relation can be separated by a maximum of 205 words.


Using 1-, 2-and 3-word sequences from the text, the strongest associations for the action clusters are shown in Figure 4, and we see that clusters 1 [come-closer] and 2 [move away] have strongest associations with "move toward each" and "move away", but these are not very dominant over other competitors.

for clusters 3 and 4 [chase], there is a strong association with the word "chase".


Estimating Probabilities for Action Maps: In obtaining frequency estimates for synonyms, we require these phrases to co-occcur with instances where a known verb appears.

even with 36 parallel narratives, the perspectival variation among speakers is such that quite often the same scene will not be focused on, and even where it is, completely unknown phrases may be used (e.g.


Also, we highlight many cases of zero anaphora, and show how these may also be inferred, most commonly as the most recent agent in the perceptual input.

this work, even though it is different from traditional discourse-only-input-based attempts at anaphora resolution, is clearly just a beginning.


In most of the attempts to learn the language to semantics mappings, semantic representations were often limited to the logical representations (Zettlemoyer and Collins, 2005;H Alshawi, 2011).

the need of much richer semantic representations such as percptual schema (Barsalou, 1999), image schema (Mandler, 1992) is argued for grounding the meanings of words (Harnad, 1990).


The process of adaptation is more successful in the case of POS-rules than 'Match', which may be related to the fact that longer units are also identified in Wiki50 and the list we automatically collected from Wikipedia probably contains more noise in the case of longer units.


silk jersey halter-neck evening dress), for which our methods were not prepared.

some of the errors are related to annotation errors, for instance, marking noun compounds that contain a proper noun, e.g.


Our adapted methods achieved better results on the target domains than the original ones as regards both noun compounds and light verb constructions.

the overall results are better for the source domain in the case of light verbs and for the target domain in the case of noun compounds.


the performance on the source and the target domains, which might be related to differences in the distribution of (a)typical light verb constructions.

'MFV' proves to be the most important feature for both domains, which suggests that with a well-designed domain-specific list of light verb candidates, competitive results can be achieved on any domain, especially if enhanced with syntactic features.


Unsurprisingly, gold standard role types lead to the largest performance gain.

it can be seen that even when the role types are first predicted automatically, noticeable performance gains can be obtained compared to not using type information at all, at least for seen data.


Furthermore, DARE is able to correct and update the rules when more queries and more documents are generated through the users.

the dependency structures in DARE system are fairly strict, therefore, not as robust as the regular expressions.


they establish between two words, not between synsets), they cannot be automatically transferred into other wordnets.

some other wordnets have derivation relations marked: the Czech one (Pala and Smrz 2004), the Bulgarian (Koeva 2008), the Russian (Azarova 2008) ones.


The contents of the links are visualized by displaying the surrounding text in another coordinated textbased visualization (not shown here).

the key to an efficient annotation is an intelligent interaction technique.


The results suggested NLP techniques can help to improve the identification of plagiarised documents.

besides being small, the corpus contained easily detectable short cases of induced plagiarism.


The high recall figure indicates that using all synsets in the similarity metric can help reduce the number of false negative cases.

the slightly lower precision indicates that using all synsets may be too lenient.


From the table we can see that in both cases the same set of features and using of machine learning methods lead to much higher values of average precision.

there are significant distinctions in ratios between AvP of features between domains.


(2005), which evaluate the effectiveness of diverse lexical, syntactic, and semantic information on RE, and Zhao and Grishman (2005), which use a more complex kernelbased strategy to combine features of different linguistic levels.

their work differs from ours in a key point: the linguistic feature space is not the same, in particular they do not make use of lexicosyntactic patterns.


It seems that no scientific study leading to an automatic titling application was published.

the title issue is studied in numerous works.


For that, using TF-IDF (Salton and Buckley, 1988) to compute the score of every extracted noun phrase from the text, and then ranking NPs according to this score, has seemed to be a reasonable way of implementing the representativity requirement.

if a new article is inserted into the corpus, TF-IDF has to be computed again.


During the last two decades, the use of machine learning has greatly improved the adaptability to new domains, or even languages.

the vast majority of machine learning algorithms operate under a basic assumption: both the training and test data should use the same feature space, and follow the same distribution, suggesting that both should originate from the same thematic domain.


In a sense, both approaches exploit information that can be characterised as "text relatedness" (or "feature relatedness"), as both "is-a" relations and correlation can be viewed as a relatedness measure between features.

our method has also some important differences with these two methods.


An electronic dictionary may be an easier resource to find than an ontology or hierarchy, thus our approach may have a small advantage in initial requirements when compared to (Gabrilovich and Markovitch, 2005).

the calculation of feature correlation has no initial requirements in resources, but requires a corpus of adequate size, in order to extract the correlated groups.


It includes several subtasks such as subjectivity detection, polarity classification, review summarization, humor detection, emotion classification, sentiment transfer, and so on [9].

most of works related to OM are oriented to use English language.


There are also possible variants in the case of Romanization of Arabic for the above example, taking into account the diacritics in the Arabic language.

a native speaker could still understand this sentence.


We have also accomplished several experiments using different n-grams models.

the obtained results with bi-grams and trigrams were very similar to unigrams.


On the one hand, systems like LTG (Mikheev et al., 1998), MUSE (Maynard et al., 2001), HNERC (Farmakiotou et al., 2002), OAK (Sekine and Nobata, 2004), (Magnini et al., 2002) and (Arora et al., 2009) use pattern-based rules.

it is worth mentioning approaches based on Hidden Markov Model (HMM) like Nymble (Bikel et al., 1997) and (Zhou and Su, 2002).


agreement on the contrary, had clear indicators: 'I agree with', 'have to agree'.

not all relations could be detected so easily; for example, justification, explanation, suggestion had less specific words and much more content words referring to the discussed topic.


The best F-score = 0.839 was achieved by SVM.

the authors used a small and imbalanced dataset, i.e., 169 positive and 74 negative sentences.


Using large training data increases the recall of the trained models with a small negative effect on precision.

the total F-measure is better when using a large corpus, rather than training our model on small training data.


Our system currently targets English and offers a wider set of functionalities to users, in addition to a software architecture which can be extended very easily with more annotation components complying with UIMA interfaces.

our system architecture is language-independent; adopting new languages is a fairly easy process as long as the relevant annotation tools and their UIMA interfaces are available.


They reach an error reduction of 28% in the target domain.

sagae (2010) shows that while the reranking approach by McClosky et al.


We also experimented with conditions where we removed one-word HCRC sentences from the additional training data.

the F-scores with one-word sentences removed were very close to their counterparts, if not somewhat lower.


Such words are often removed in text classification.

they played an important role in the authorship attribution task (Zhao and Zobel, 2005).


In general, Naïve Bayes performed better on the vocabulary features than on the orthographical ones; the reverse was true for KNN.

naïve Bayes outperformed K-nearest neighbor on the orthographical features as well.


The algorithm outperformed NB and KNN on both the forums.

the specific PPM methodology of feature use makes much more difficult the comparison of the influence of specific text features on the author attribution task performance.


country names, days of the week, months, first names).

the TwitIE POS tagger does come with customised in-built gazetteer lists, used for tagging unambiguous named entities, e.g.


TwitIE also outperforms Ritter's Twitter NER algorithm (Ritter et al., 2011) and our adaptation of the Stanford NER, which we trained using both tweets and newswire (see (Derczynski et al., 2013a) for details).

as shown in Table 1, when compared against state-of-the-art NER performance on longer news content, an overall F1 score of 80% leaves notable amounts of missed annotations and false positives.


Romanian), the task of grapheme to phoneme conversion is significantly easier and more accurate than for many other languages (such as English).

there are several issues, common to several languages.


Our training data was extracted from the Romanian Speech Synthesis Corpus (RSS) (Stan et al., 2011) and it is comprised of a small number of words (8K).

due to the preponderantly phonetical nature of Romanian, this number seems to be sufficient for training a highly accurate L2S data-driven method.


Then, the application of our classification rules further filtered those patterns, yielding a list of 6,612 contextual valence shifter candidates: 2,607 were labeled as INT, 2,677 were identified as INV, 1,328 were classified as CONC, and 216 were assigned to more than one categories 5 .

among those candidates, only 1,147 structures received a score of 5 or higher.


The CONC class contains structures that often relates terms with different polarities.

it is worth considering that diminishers are often used in concessive or rhetorical structures and assign them to the class CONC rather than to the class INV.


as the maintenance of this language version has been stopped, the latest version 2.0 contains only a subset of terms (n=10,003).


A few forms of expression make up for the bulk of occurrences of temporal expressions.

existing research has been typically evaluated on only a small corpus of hand-annotated temporal expressions.


These perform reasonably well on existing datasets, achieving F-scores of around 0.90, and improving them is an active area of research.

as temporal annotation is expensive, existing datasets are not particularly large, and therefore do not contain as challenging a variety of forms of expression as general, unannotated text.


Unfortunately, in few cases we were not able to find the appropriate movie given the review, thus the resulting dataset slightly differs from the one from .

we report all results on the new dataset (where the reviews are paired with their movies) and also provide it for any further research.


It would be beneficial to group the allomorphs in the same cluster by assigning the same morpheme label as described before.

to allomorphs, some morphemes might sound the same phonetically; however, they might function differently.


It should be noted that using previous and following morphs in English is not very beneficial due to the simple morphology of the language.

we used these two features because of a number of words having more than one morph.


The presented algorithm can find allomorphs in Turkish by clustering them together.

as far as we could observe from the results, it cannot show the same accuracy for homophonous morphemes.


In this case, the accuracy with which the system assigned to a chunk the name of the document from which it was extracted was only 72.1%.

the misclassifications happen mostly within century level.


Therefore, translations into multiple languages are useful for the case of auf.

the best systems are those without any translation evidence from Spanish or English.


Table 8 shows that most feature sets have a beneficial effect, and therefore, classification performance is almost doubled by the best system.

to the syntacto-semantic classification, multilingual evidence does not contribute to the best system.


The advantage of working at the token level is that the high-frequency words and abbreviations can be translated in context, which outperforms a simple dictionary look-up (Raghunathan and Krawczyk, 2009).

working at the character level allows one to generalize over character mappings which makes the system more robust (Pennell and Liu, 2011).


Part-of-speech information is a prerequisite in many NLP algorithms.

twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.


from 73.37% to 83.14% for the Stanford tagger), showing the impact of tweet-genre training data.

the improvements are much less impressive on D-dev, which is a completely different corpus.


The expected F measureF is also considered in (Nan et al., 2012), where also its consistency is studied and even a Hoeffding bound for the convergence is given.

the authors there mainly concentrate on the acceptance threshold to optimize the F -measure.


On the other hand all solutions of the MOP can be obtained by maximizing nonnegative linear combinations of the objectives (Ehrgott, 2005).

a nonnegative combination of the objectives {log p(y(1)|x(1), λ), ..., log p(y(m)|x(m), λ} is precisely the weighted maximum entropy objective function.


*)tȃ$ Table 2: Example of rule overlap in the unstructured system (Dinu et al., 2012) learned by a linear SVM with 90.64% leave-oneout accuracy.

when taking a closer look at the labelling rules described, a considerable amount of overlap can be spotted, in terms of what alternations the rules model.


This problem is especially serious in short sentences.

the weight of each mismatched word is small when the number of words is large.


Like entities, acronyms have a high reference value, in the sense that they most of the time act as reference anchors of textual content.

they are not always explicitly defined, which can cause comprehension problems, both for humans and machines.


The next column shows that the ratio of news articles AS in which good acronyms (acronyms passing the filtering process) were found, compared to all news articles analysed (AA), is 13%.

there are enormous differences from one language to the other, with Spanish, Latvian and Portuguese having the highest density of acronyms and Finnish, Swedish and Basque having the lowest.


In examining the results, we find that the correlation of our experiments with PYRAMID, at the micro-evaluation level, is not high enough; in spite of this, it is greater than standard metrics and ROUGE-1.

our experiments give a good correlation with PYRAMID at the macroevaluation level.


Pembe and Güngör (2010) focus on DOM tree and formatting features, but also use occurrences of manually crafted cue phrases such as back to top.

most features are only applicable in very few cases where HTML markup directly provides a hierarchy.


The overall level of performance in terms of accuracy and Rouge seems rather low.

accuracy is only a rough estimate of the real performance, as many good titles might not be represented in the gold standard and Rouge is higher when comparing longer texts.


In original SSK, an alphabet letter is assumed as a comparing unit that can be expanded to sub-string by increasing the K value.

in this study the comparing unit has been changed to POS and/or dependency labels.


the cats slept contains further (distinct) instances of the and cats.

the instances in a repetition of the cats sat on the mat are the same as those in the original (because instances are defined relative to strings, that is, string types not string tokens).


The primary concern of these systems is to help the user make stepwise improvements to a piece of writing.

the primary concern of our system is to promote self-regulated learning, self-knowledge, and metacognition.


Now we would like to add exp[λ lm log(p lm (t to the label of each transition associated with t i .

the problem is that there may be multiple sequences of preceding words t i−n+1 t i−n+2 .


The explosion of data in all forms from blogs, online forums, Facebook, Twitter and other social media channels has given an opportunity of unprecedented reach to publicly sharing thoughts on events, products and services.

there are some open issues related to this research area, commonly known as Opinion Mining, which can be summarized as follows: (1) Opinions are potentially ambiguous, and (2) Contextual interpretation of polarity is hard to achieve.


In Somasundaran and Wiebe (2009) the authors try alternative machine learning approaches with combinations of supervised and unsupervised methods for the same task.

they do not automatically identify discourse relations, but used task-specific manual annotations.


The authors used only contrast and no contrast discourse relations to achieve their results, conducting a survey on a small amount of data that showed that the contrast relation was the most frequent one.

the survey presented in Hatzivassiloglou and McKeown (1997) on the WSJ corpus showed that contrast is actually the third most important relation in the corpus.


We used this tool because the output of CRF++ is compatible with CoNLL 2000 chunking shared task, and we view our task as an opinion expression chunking task.

linearchain CRFs for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position.


Altogether, the results are unlikely to get as accurate as this can be done by the use of learning procedures.

if transferring is working reasonably well to domains where learning methods are not applicable -due to lack of corpora, we can potentially achieve a big gain: decision criteria are grounded in abstractions from empirical data, which is superior to using hand-crafted rules.


Furthermore, classifiers trained with boosting-based learners have shown faster classification speed (Kudo and Matsumoto, 2004) than Support Vector Machines with a tree kernel (Collins and Duffy, 2002).

existing boosting-based algorithms for semi-structured data, boosting algorithms for classification (Kudo and Matsumoto, 2004) and for ranking (Kudo et al., 2005), have the following point that can be improved.


Generally, "I won't read" seems to be used in negative reviews.

reviews in training data include "I wont' read" more than once is positive reviews.


Although we had 413,814 positive instances available in the Enron Threads Corpus, we found that classifier performance was unaffected by the amount of training data, down to very low levels (see Figure 1).

because the standard deviation in the data did not level out until around 1,200 class-balanced training instances 5 , we used this number of positive instances (600) in each of our experiments.


both the RB and SB datasets use a class-balanced distribution.


The configuration with all features showed a 15 percentage point drop in precision, and a 12 percentage point drop in accuracy.

content features continues to be the best performing feature group with semantically similar negative instances, as with random negative instances, and outperformed the Dice baseline.


All TP instances were judged to have a professionalism match, as well as 94% of FN's.

only 64% of FP's and 56% of TN's were judged to have a professionalism match.


For example, some erroneous word combinations may not be completely devoid of compositional meaning, while violating language conventions.

semantic models might still be able to capture some of these conventions.


Our results suggest that with the metrics considered it is easier to detect the difference between the model-generated vectors for the correct and incorrect word combinations with the multiplicative model.

qualitative analysis suggests that the adjective-specific linear maps of Baroni and Zamparelli (2010) are superior, since they place the model-generated vectors in semantically sensible neighbourhoods.


(2011) showed that, at least for German, Malt- Parser suffers from a fairly small decrease in accuracy if only features from the next two words instead of three are used.

since the PoS tags are needed and a PoS tagger needs lookahead as well to achieve good accuracy, a lookahead of at least three words is needed for the whole taggerparser pipeline to achieve a high accuracy.


Our work is conceptually similar to that of de Gispert and Marino (2008), in which they incorporate a morphological classifier for Spanish verbs and define a collection of context dependent linguistic features (CDLFs), and predict each morphology feature such as PER or NUM.

we use a different set of CDLFs and incorporate DTC to predict the morphology features of Persian verbs.


Many sentiment-analysis methods for the classification of reviews use training and test-data based on star ratings provided by reviewers.

when reading reviews it appears that the reviewers' ratings do not always give an accurate measure of the sentiment of the review.


Their work is similar to ours because of the focus on the effect of the review text on the customer/reader, but they also include other types of information such as transaction data, consumer browser behavior and customer preferences.

none of these studies focus on the relationship between reviewer rating and review text.


Agreement on the 4 class annotations is much lower (κ >= 0.51) showing that polarity strength is difficult to annotate.

given the purpose of this study, we are not interested in agreement as such.


since reviewer ratings are widely available and come for free with the text, they will often be used to evaluate the tools.


For K source domains, the augmented feature space consists of K+1 copies of the original feature space.

creating three versions of each feature in both the source and the target domains grows the feature space exponentially, which is prohibitive in a many-domain adaptation scenario such as ours which consists of a total of 27 domains.


it is positive in the food domain while it is negative in the negative domain), contributing to the need for domain adaptation.

the success of the all-in-one classifier indicates that a greater number of observed sentiment features and more solid statistics on those features are more important than capturing domain-specific polarity changes.


This could have led to some bias concerning the selection of the question-initial words.

the mechanism seems to work surprisingly well and it is sufficient to give two words to obtain a lot of sensible questions.


On one hand we are still working on a set of rules for recognition of 8 categories of semantic relations between PNs for Polish.

we are still extending the WCCL Relation toolset with 6 http://nlp.pwr.wroc.pl/redmine/ projects/corpus2/wiki 7 http://www.nlp.pwr.wroc.pl/wccl.


In Ger-maNet, I-hyponymy has quite few instances.

the second largest relation in Ger-maNet is I-near synonymy.


UDOP works based on the repetition of possible sub-trees of parse trees of the input sentences.

in free word order languages such as Persian, words can grammatically appear in different places of sentences.


As 1 http://www.ncbi.nlm.nih.gov/pmc/ 1.

to [1], we found clear detrimental effects of prophylaxis.


This has several applications such as detecting consumer opinions in on-line product reviews or increasing the effectiveness of social media marketing campaigns.

the informal features of Web 2.0 texts can affect the performance of automated opinion mining tools.


Also, microblogging platforms such as Twitter include rich metadata about interactions which provides a way to measure the reputation of their users based on the number of followers or the publication popularity by counting the number of times one message has been shared.

the language used in social media is very informal, containing elements such as misspellings, slang, lexical variants, inconsistent punctuations, URLs or emoticons (Thurlow, 2003).


These contain a very low amount of lexical variants and misspellings, and because of that using lexical normalisation techniques can lead to false positives in the substitution process, thus decreasing the performance of the polarity classifier.

the microblog dataset is substantially more informal, so the application of normalisation techniques has a positive impact in the classification results.


Factor F4 is considerably less used in mother tongue, that shows the requester's knowledge of politeness structures in FL with factor F4 -a politeness marker (with words such as please or thank you) -formulated in requests in comparison to MT.

factors F5 and F7 are much more used in FL.


This time, select-first does not work at all: it selects MT050, which is the worst possible choice (while it was best in the reverse, MT04-MT05, translation direction).

the concat-all strategy performs reasonably well: it would be second best if ranked together with the individual inputs (it was third best in the reverse direction).


The vast majority of the phrases in both languages were associated with only one translation.

2857 phrases in German and 5131 Ital-ian phrases have been associated with multiple translations (respectively 2.3 in average for both language).


In general the results are promising since the recall in patent search is more important than the precision, and thus the noise is not so disturbing.

much work remains to be done for improving the computation paradigm and refining the precision.


While a 10-million-word portion of their English newspaper corpus has less than 100,000 different word forms, a corpus of the same size for Finnish contains well over 800,000.

however, while an open class English word has no more than 4-6 different forms, it has several hundred or thousand different productively suffixed forms in any of the agglutinating languages discussed in that paper.


(Snow et al., 2006), weights are interpreted as probabilities.

many relation extraction methods are not based on statistics, and word-pairs extracted by them cannot be described by probabilities, e.g.


Being a special case of topic classification, it can benefit from all well-known classification algorithms.

as sentiment classification relies on sentiment markers rather than frequent topic words, it potentially needs more data for satisfactory performance.


The decision rule forŷ i to belong to the class 1 can then be represented as: 2.2 Modifications to the LP algorithm The graph structure used in LP does not differentiate between labeled and unlabeled neighbours.

in some cases it might be beneficial to give them different impacts.


For example, in SSL it is natural to rely more on labeled data whose labels are identified with a high level of confidence.

for CDL highly reliable labels do not help much when source and target data are very different and it might be better to prioritise unlabeled examples.


This difference has no effect as long as the scaling factors for both matrices are similar.

this might not be the case for cross-domain graphs.


Empirically, augmenting the chunked ITG with rules learned via top-down segmentation helps translation quality.

the maximum likelihood objective is inadequate for this purpose; instead, we introduce the minimum conditional description length (MCDL) objective to drive the search for phrasal rules simultaneously from the two opposing types of ITG constraints, both of which have individually been empirically demonstrated to match phrase reordering patterns across translations well.


(2010) used a combination of semantic path length, node depth in the hierarchy, and the types of the semantic edges that compose the path.

corpus-based approaches rely mainly on distributional properties of words learned from a large corpus to compute semantic relatedness.


According to this, most taggers take into consideration the context of a word to decide which should be its tag.

each of the words belonging to a word's context can also be used in different ways, and that means that, in order to solve the problem, a tagger should have some type of disambiguation mechanism that allows it to choose the proper POS tags for all the words of a sentence.


The best accuracy result returned by the GA-Tagger is worst than the best result obtained with the PSO-Tagger and it needs the double number of evaluations required by the PSO-Tagger.

the accuracy values displayed by the GA-Tagger are still very competitive when compared with others published using similar approaches.


As expected, the results achieved by the two taggers on the WSJ corpus, using as heuristic the disambiguation rules learned from the Brown corpus, are inferior to the ones obtained on the Brown corpus.

we believe that they allow us to conclude that the discovered rules are sufficiently generic so that they can be used in different corpora.


Bobicev et al (2012) studied tweets that reveal PHI.

their work was focused on sentiment analysis of these tweets.


The extraction method looked for tweets with references to H1N1 and its synonyms.

numerical evaluation of the method was not reported by the authors.


More waiting, but getting more hopeful is a description of symptoms and should not be annotated as subjective.

i hope your visit with us infertilies is short and sweet and you get that baby soon!!!


This study is certainly more accurate wrt previous similar works that use few manually defined keywords (Althouse et al., 2011), such as flu and influenza.

first, the algorithm depends on the availability of critical resources: web query logs are a kind of data which is not freely available.


To our knowledge, trying to automatically learn these automata instead of writing them by hand has never been tested for any language before.

chunking can also be treated as an annotation task (cf.


HMMs, which were the previous state of the art for annotation tasks, have the merit to be more understandable.

every discrete HMM can be "transformed" into a CRF model defining exactly the same probability distribution (Sutton and McCallum, 2006;Tellier and Tommasi, 2011).


The A sub-categories topics include: agriculture (A01), clothes and footwear (A41 and A43), furniture (A47), and firefighting (A61).

the H sub-categories are restricted to only electricity-related topics.


the set of all patents, the A class, and the A61 subclass of A slow in growth after about 400,000 tokens have been observed and after 1,000,000 tokens have been observed have a much smaller absolute number of types than the BNC.


The results of this study hold out the promise of further development of semantic search for patents.

they make it clear that this will be a broad problem, with the necessity to tackle different classes of patents separately, confirming the findings of Oostdijk et al.


1 Introduction and Related Work The term sublanguage has various definitions, depending on criteria that will be discussed in a moment.

descriptions of the sublanguage phenomenon generally have two things in common.


Simple search in Google Scholar shows that most frequently used models for document classification are Naive Bayes, linear SVMs (Cortes and Vapnik, 1995), Perceptrons (Rosenblatt, 1957) and Maximum Entropy (Berger et al., 1996) (Figure 1a).

non-linear models such as non-linear SVMs and Random Forest (Breiman, 2001) and Classification trees (Breiman et al., 1984) are significantly under-represented.


The most common values for l opt are 0.25% and 0.1%, which means that the useful conjunctions are those that comprise at least one high scoring atomic feature (from top 0.01% or 0.25%).

a larger l opt would mean that the model benefits from conjunctions between two low scoring atomic features, which is not the case, according to our results.


On the contrary, our experiments demonstrated that in-domain comparable data are strongly preferable to outof-domain parallel data.

there is an optimum level of similarity between the comparable sentences, which according to our similarity metrics (for the language pairs we worked with) is around 0.4 or 0.5.


The authors elaborated methods for detecting and correcting OCR problems in Urdu-English digital dictionaries using Dictionary Language Modeling (DML).

these dictionaries are poorly structured resulting in the digitalization of paper versions.


Filtering Noises: Fuzzy matching could discard about 99.5% data of the general corpus without reducing translation quality.

other methods will drop their performance when using the same size of data.


The difficulties, raised in the manual creation of Treebanks, led the researchers to use automatic and semi-automatic methods of treebank development methods.

the automatically extracted Treebanks are not as accurate as manual versions.


We selected C4.5 among decision tree classifiers as it is a typical classification approach and has some superiorities to ID3 such as handling missing values and different feature costs.

there have been a number of studies on extending Adaboost to imbalanced datasets ( Japkowicz and Stephen, 2002).


Such complex cases cannot be covered by a simple heuristic, but rather need to be defined via language dependent rules in order to be captured properly across languages.

as mentioned before, this requires linguistic knowledge of the language in question.


Our results for the POS tagging task are aligned with the current state-of-the-art for Bulgarian.

a direct comparison to (Georgiev et al., 2012) is not possible, since their POS tagging component was trained on the morphosyntactic subset of BulTree-Bank that is two times larger than the dependency subset we used, and it was evaluated against a different collection of test sentences.


The annotations available in our dataset are also comparable to theirs: POS, morphosyntactic information, heads, dependency relations, named entities, etc.

semantic roles are missing in BulTreeBank.


morphdb.hu is one of the most widely used morphological databases for Hungarian, which makes use of the KR morphological annotation system (Trón et al., 2006a).

the only manually POS-tagged corpus, the Szeged Corpus (Alexin et al., 2003) is annotated with MSD codes.


Recall the example of csináltathatjátok (make-CAUS-MODAL-2PL-OBJ) "you can have it made", where the lemma is csinál "make", the derivational suffixes -tat and -hat denote causativity and modality, respectively, and the morphological code of the word form includes information on causativity and modality as well.

no derivational information is marked in the case of the denominal verb kezel "treat, han-dle", which is derived from kéz "hand", since this information is irrelevant from a syntactic point of view.


One of the first morphological analyzer developed for Hungarian was Humor (Prószéky and Tihanyi, 1993).

the tool is not freely available and is not open source.


Although there are a handful of morphological taggers for Hungarian, their performances are not directly comparable since they rely on different coding systems.

the harmonized morphology (see Section 3) enable us to build a morphological parser, which is now integrated into magyarlanc and the output of which is in total harmony with the Szeged Corpus.


For instance, a noun ending in the -nak/-nek suffix may be in the genitive or in the dative case, thus the MSD codes Nc-sd (a singular noun in the dative) and Nc-sg (a singular noun in the genitive) will be reduced in a different way.

the codes Nc-sd and Nc-sd---s3 will be reduced to the same form since there is no such Hungarian lemma that would have the same word form for a dative singular and a dative singular with a third person singular possessor (and thus, the POS-tagger would not have to choose between these possibilities).


This can be inferred from high accuracy of stateof-the-art POS tagging not only for English, but also most other languages such as Arabic, which reaches 97% for Arabic and English being at 97.32% (Gadde et al., 2011).

the performance of standard POS taggers for English is severely degraded on Tweets due to their noisiness and sparseness (Ritter et al., 2011).


Most of the limited available techniques have employed Machine Learning (ML) algorithms, such as Support Vector Machine-based (SVM) methods.

our approach is based on a manuallycreated lexicon enriched with sentiment scores; coupled with hand-coded grammar rules.


The approach utilises the SentiStrength library, which applies a combined method to detect the polarity and strength of short informal social texts.

as this library was designed around the English language, the authors rely on the translation of the core resulting list to Persian.


Also deleted questions have a lower percentage of code blocks than closed questions (Correa and Sureka, 2014).

the presence of a code snippet may also have adverse effects as well if the code is hard to follow or if other users cannot see the problem (Asaduzzaman et al., 2013).


In the current study lexical entities, the terms, are included to predict question quality above the level of the assigned tags.

the terms were analyzed manually, based on human judgment.


The domain dependent group includes Word-Based and Synsets features described in Section 4.1 and 4.2 often used in text classifications and topic recognition tasks.

the domain independent features are not strictly related to the topic of the message.


With cross-domain experiments we confirmed the second hypothesis, showing that word-based features are not robust when the topic of training and test set are different.

domain independent features do not decrease their performance when training and test do not share the same topics.


On the other hand domain independent features do not decrease their performance when training and test do not share the same topics.

in the SENTIPOLC task domain dependent features were relevant, and detecting the topic of a specific class was important.


In the light of these results, we suggest that if a Sentiment Analysis system has to recognise polarity cross-domain should avoid word-based features and focus more on features that are not influenced by the content.

if the a Sentiment Analysis system is used in a specific domain, words may have an important role to play.


Spell-and grammar-checking is not a new task, and is integrated in many standard text editors for the major languages.

smaller languages are not so well covered, and the technology is very much inspired by what works for English where simple list checking will identify non-words, and correction suggestions can be found with the editing distance measure using the same list.


However, smaller languages are not so well covered, and the technology is very much inspired by what works for English where simple list checking will identify non-words, and correction suggestions can be found with the editing distance measure using the same list.

the task is more difficult for morphologically rich languages, where word formation is too productive to allow lists with good coverage.


For correctly typed errors, the right correction alternative was chosen in over 95% of cases.

performance for grammatical, conditioned errors is not on par with the system's accuracy for classical spell-checking, and should be improved.


Using ngrams, the difference of results grows when the number of authors increases.

using motifs seem to be adapted to this issue.


Much of the research on ED has been based on the specifications of the 2005 ACE [Automatic Content Extraction] event task 1 , and the associated annotated corpus.

as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE events do not appear in the training data, adversely affecting performance.


Most types of events can be expressed by verbal or nominal constructions.

in a number of cases the ACE training corpus includes the verbal construction but not the corresponding nominal one.


This hierarchical clustering has recently been used in thousands of computational linguistics papers, often for feature generation.

no work exists describing the behaviour and hyper-parametre tuning effects of Brown clustering; even the original paper concentrates on implementation rather than its behaviour.


So, the information added by clustering increases with c > 1, peaks, and then declines towards |V |.

the information added solely by the hierarchy increases with c and peaks when every word type has its own cluster, i.e., when c = |V |, as this gives the maximum number to the tree; we cannot add more leaves than there are word types (given a single root).


For example, a restaurant may get a negative score for the aspect service but a positive score for the aspect price.

we assume that a user has a distribution over aspects to indicate their relative weight when the user rates a product.


The major goal of our evaluation is to see how well the PAR model can identify and separate product properties and aspects.

in all three data sets we use, there is no ground truth and we are not aware of any data set with ground truth labels we can use for our task.


Summarizing opinions expressed in online forums can potentially benefit many people.

special characteristics of this problem may require changes to standard text summarization techniques.


We observe that there are many short sentences in online forums and most of them do not carry much useful information.

long sentences appear to be more informative and more useful.


The idea is to maximize the overall score of selected sentences while also minimizing the redundancy among selected sentences.

his method can have an exponentially growing number of parameters and it cannot globally measure redundancy.


This shows that our modified versions of the objective function can effectively bring in longer sentences for summaries.

the two modified methods based on sentence subjectivity have very different performance.


We can also observe that precision drops more significantly for some categories such as Liquid and Document.

table 8: Precision for S3+ on HUM-RELAXED when compared to results presented in Section 4.1, we can see a clear improvement, possibly due to a non-optimal mapping between the CPA Ontology and WordNet, but also explainable by St members correctly extracted from the web, but absent from WordNet.


Each Sub-categorization Frame is composed of different arguments, represented by the Syntactic Argument class, which allow its connection with the SynSemArgMap instance class.

syntactic Behaviour is the class that describes one of the possible behaviours of a lexeme and it can be attached to the Lexical Entry instance and optionally to the sense instance.


As noted in Section 3, the initial T S consists of the WCL dataset, which makes our model suitable for DE in well-formed encyclopedic texts.

our hypothesis that it would perform poorly in a linguistically more complex setting (e.g.


Interestingly, the feature-update step has a dramatic influence in performance in both corpora: On one hand, the performance peak in a dataset with less linguistic variability (MSR-NLP) is reached early, and after iteration 100, where the feature update step occurs, Precision decreases, while Recall remains the same.

the numbers in the W00 dataset are fairly stable until iteration 100, where a significant improvement in both Precision and Recall is achieved.


Let us look first at the results without applying recall-boosting post-classification heuristics: The performance of our models decreases in the MSR-NLP corpus after a few iterations (our best model is reached at iteration 23, where F=76.23), and this situation is unsurprisingly aggravated by the feature update step.

our results improve significantly in the W00 dataset 6 after feature updating.


We have shown that our DT-based approach adds more than double the amount of absolute F1 points on bridging mentions in the test set than the semantic features described by Bansal and Klein (2012).

undesired semantic relations present in the DT lead to a decrease in general resolution precision.


The kernel ability to generate large feature sets is useful to assure we have enough linguistic features to differentiate between the classes, to quickly model new and not well understood linguistic phenomena in learning machines.

it is often possible to manually design features for linear kernels that produce high accuracy and fast computation time whereas the complexity of tree kernels may prevent their application in real scenarios.


The polysemy of conduire (drive/take/lead) prevents from isolating the semantic class of transport.

with another appropriate predicate réparer (repair), we can predict that the arguments which can appear after both conduire (drive/lead) and réparer (repair) belong to the semantic class of transport.


In the semi-supervised method, the predicateargument relations are extracted from a set of arguments.

with a set of appropriate predicates given in advance, the syntacticsemantic distribution can be predicted, but from the arguments, it is not certain to predict which syntactic-semantic relation is associated with the given argument.


An interesting approach for deep syntactic and semantic disambiguation was presented by Jackov (2014).

the paper indicated that no evaluation of the system has been made.


The approach of Jackov is similar to the above-mentioned method in obviating the POS tagging step and the simultaneous resolution of all the morphological ambiguities together with the syntactic and semantic ambiguities.

all of the linguistic data it uses is defined explicitly and only the dependency relations knowledge database may be automatically populated, while most of the other approaches rely on machine learning taking arbitrary features from training datasets.


It is worth mentioning that the error analysis lead to discovering errors in all resources used by the system.

the goal of this paper is to evaluate the system using BulTreeBank, that is why the errors found in other resources are not discussed.


For instance, той прави често това, meaning 'he often does this'.

this is unacceptable when the direct object is a personal pronoun clitic.


Days of month are normally annotated as ordinal numerals and a singular noun for the month.

many numerals representing a day of month were annotated just as M.  Inconsistent annotation of часа (meaning 'o'clock').


Our re-implementation of the arc-standard algorithm, which is a generalisation of propos-als by Kuhlmann and Nivre (2010) and Attardi (2006), will handle all possible cases of nonprojectivity because we allow N in LEFT-ARC(N) and RIGHT-ARC(N) to be a positive number larger than 2 if necessary.

it contrasts the approach used by Kuhlmann and Nivre (2010) in that we combine the head of the queue with any item on the stack rather than combining the top items on the stack.


The linguistic information in PATB is sufficient for inducing a parser.

the limitation for using this treebank directly for generating a parse model is that its annotation schemata is based on a phrase structure format, which cannot be used for dependency parsing.


Applying both extensions to the parser did not lead to better results than using them individually.

applying both extensions lead to better parsing accuracy than using none of them but the parsing speed degraded by about 36%.


They used the Penn Treebank tagset, which is comparable to our fine-grained tagset, but since the CMI value for their English-Spanish corpus is not known, it is hard to compare the performance figures.

followed the same strategy as Solorio and Liu (2008b), reporting an accuracy of 74.87%, also given that the word-level languages were known.


This is very different from sentiment analysis on Twitter, where three-way sentiment classification schemes (positive, negative, neutral) have been preferred, e.g., at SemEval 2013-2015 (Nakov et al., 2013;Rosenthal et al., 2014;.

the star system makes the task more fine-grained, thus allowing to capture user opinion better.


This is expected, as classification usually struggles with too many class labels, especially in the case of uneven class distribution, and this is indeed our case, as we have seen in Figure 1.

the low performance of ordinal regression is quite surprising; the expectation was that it would perform the best.


In addition, the results in Table 6 show that the effect of the domain adaptation varies according to the genre of the corpora; only the Q&A site data could improve the F-measure of the system without filtering.

the other results in this table show that four-fifths of the system improved the F-measures.


The most common features include character n-grams, function words, parts of speech, spelling errors, and features of writing quality, such as grammatical errors, style markers, and so forth.

swanson and Charniak (2012) introduced the Tree substitution (TsG) structures, learned by Bayesian inference.


For PerCal, the pattern score at rank 500 is 0.65.

to PerTime, precision is decreasing at a slower rate with lower-ranked events.


It is the same for fumier (dominant sense: manure, substituted sense: insult), cellule (cell) (dominant sense: biology, substituted sense: jail cell), etc.

the refinements of these terms show a polarization consistent with that expected.


Such polarities are largely positive and are twice the number of neutral and negative polarities altogether.

for higher distributions (from 60% to 80%) number of positive drops sharply.


Non-standard linguistic features have been analysed both qualitatively and quantitatively (Eisenstein, 2013;Hu et al., 2013;Baldwin et al., 2013) and they have been taken into account in automatic text processing applications, which either strive to normalise non-standard features before submitting them to standard text processing tools (Han et al., 2012), adapt standard processing tools to work on non-standard data (Gimpel et al., 2011) or, in task-oriented applications, use a series of simple pre-processing steps to tackle the most frequent UGC-specific phenomena (Foster et al., 2011).

to the best of our knowledge, the level of (non-)standardness of UGC has not yet been measured to improve the corpus pre-processing pipeline or added to the corpus as an annotation layer in comprehensive corpus-linguistic analyses.


For the technical dimension, the most prominent features are the ratio of alphabetic characters, the number of character repetitions, the ratio of upper-case characters and the ratio of spaces after punctuation.

for the linguistic dimension, the most prominent features are the OOV rate given a standard lexicon (sloleks), the OOV rate given a lexicon of non-standard forms (nonstdlex), and the ratio of short tokens.


Lexical resources, particularly those that are automatically acquired, might include noisy data, which can negatively affect performance.

manually developed resources such as WordNet (Fellbaum, 1998) are particularly complex (i.e.


In our case the STS 2012, 2013 and 2014 datasets are different from the STS 2015, which leads to an overfitting of the systems that builds the model using all these features.

algorithms which are not so optimized can use MT metrics in a more flexible way to obtain good result on the test dataset.


erally achieve high accuracies of 95% or higher.

it cannot be assumed that these tools will achieve similar levels of accuracy on learner data, a distinct genre which they were not trained on.


This, alongside the higher accuracy, could potentially be a result of the tags being manually corrected by annotators, leading to more accurate tags and thus classification accuracy.

this does not entirely explain why performance degrades when using trigrams.


In the case of DVP (step 4 for our methodology), we obtain satisfactory precision values, demonstrating that if we include the BP in the DVP we can solve the problem of low precision.

the covering values are decreasing to a 18%.


The assumption was that some of these "mentioned" trolls could have actually been paid.

this is much of a witch hunt and despite our good overall results, the training data is not 100% reliable.


Distributed representations of words have boosted the performance of many Natural Language Processing tasks.

usually only one representation per word is obtained, not acknowledging the fact that some words have multiple meanings.


Although the overall precision of this procedure is not as high as with monosemous units, it yielded a rather satisfactory number of both new synset-variant pairs and new synsets.

this method significantly contributed to the improvement of the CroWN's coverage of lemmas from various Croatian corpora.


NLIDB systems translate the user's natural language (NL) query into a SQL query, thereby allowing the user to retrieve the answer from the database.

nLIDB systems are not widely used because of their inability to process ambiguity and complexity of natural language, which makes them more error prone.


(Pečina, 2010), including tests of a very large number of MWE extraction methods.

those tests were done on relatively limited data set.


All extracted and pre-filtered candidates were used during the extraction process.

the final ranking was created by post-filtering based on a narrow subgroup of only 6 WCCL pattern related to nouns and adjectives.


We have verified and confirmed the idea of Pečina of combining together many simple association measures.

tests were done on much larger corpora and a lager set of manually described MWEs.


That is, the checker offers all possible <verb> + exam collocations found in a reference corpus or dictionary.

the display of a mere list of correct collocations of a given keyword is unsatisfactory for learners since they are left alone with the problem of picking the right one among several (potentially rather similar) choices.


However, this is not to say that the two main interpretations of the term "collocation", the distributional and the idiosyncratic one, are disjoint, i.e., necessarily lead to a different judgement with respect to the collocation status of a word combination.

: two lexical items that form an idiosyncratic co-occurrence are likely to occur together in a corpus with a high value of Pointwise Mutual Information (P M I) (Church and Hanks, 1989): (1) The P M I indicates that if two variables a and b are independent, the probability of their intersection is the product of their probabilities.


Our studies show that 'Substitution', 'Creation' and 'Different sense' errors are the most common types of miscollocations.

learners tend to make rather few 'Synthesis' and 'Analysis' errors.


When developing an Automatic Speech Recognition (ASR) system it is typical to evaluate system performance by calculating quantitative measures like accuracy, F1 score, Word Error Rate (WER) etc.

in order to improve ASR performance, it is important to understand which factors are most problematic for recognition, identify the types of errors, their main causes and how critical these errors are.


We also tried to increase the lattice beam so that fewer paths are pruned.

despite a decrease in the percentage of pruned words, no improvement was observed.


This resulted in WER reduction of 0.86%, and 61% of previously out of vocabulary words were correctly recognized.

such a large increase in language model and vocabulary size resulted in the language model perplexity increasing on testing utterances by 647 compared to 498 obtained with language model trained on 22M sentence corpus and using 200K unit vocabulary, so the WER reduction was not as great as anticipated.


1 The output of SRL systems have proven to offer a good approximation to a deeper semantic modeling of natural language.

given its inherent complexity, recent efforts for improvement have tried to extend traditional SRL from the sentence-internal context to the surrounding discourse.


In accordance with previous iSRL studies, we evaluate our model on the SemEval data set (Ruppenhofer et al., 2010).

to the best of our knowledge, this is the first study to focus on the PropBank version of this data set.


Clearly, the best results are obtained by Laparra   (2012, L&R).

they only report accuracies for the identification of DNIs, as INIs are beyond their scope.


Current Machine Translation (MT) technology has serious limitations: there are, on the one hand, the rule-based systems which require handcrafted linguistic rules and their manual construction is time consuming and expensive, and, on the other hand, the statistical systems which try to learn how to translate by analyzing the translation patterns found in large collections of human translations and these systems are effective only when large amounts of parallel corpora are available.

parallel corpora are only available for a limited number of language pairs and domains.


The sense annotation process over BulTreeBank (BTB) was organized in three layers: verb valency frames (Osenova et al., 2012); senses of verbs, nouns, adjectives and adverbs; DBpedia URIs over named entities.

in the experiment presented here, we used mainly the annotated senses of nouns and verbs (together with the valency information), as well as the concept mappings to WordNet.


In (Bobicev et al., 2015a) we analyzed message sequences and found patterns of sentiments in the consecutive posts.

many posts were addressed to the one specific interlocutor by her name.


They report promising results in learning how to translate from 'original' to 'simplified' language using the standard phrasebased translation model.

our results indicate that this approach works well only when the training dataset consists mostly of those sentence pairs in which the simplified sentence is already very similar to its original.


The same substitution was done by human editors.

the system only performed this one substitution, while the manual simplification encompassed one additional lexical simplification.


In the first case, the original word was replaced with the word with a different part-of-speech (a noun replaced with an adjective).

this example (s3) also shows a particularly interesting case of lexical simplification performed by the PB-SMT system, but not performed by the human editor.


Ideally, the LM should be trained on a large corpus of "simplified" Spanish.

as such a corpus has not been compiled yet, we trained the LM on a subset of the Europarl v7 Spanish corpus (Koehn, 2005) using the SRILM toolkit (Stolcke, 2002).


In the second example (2), the SMT-based systems performed one good lexical simplification (which was not performed by the Simplext system) by replacing the word "aseguró" (assured) with the word "dice" (says).

our systems also applied one incorrect lexical simplification which, although it did not change the original meaning of the sentence, led to the ungrammatical output.


The same word was left unchanged by the Simplext system.

it appears that human evaluators tend to give a higher simplicity score to the system which performs sentence splitting than to the system which performs lexical simplification (in the case that each of the systems performs only one of the two possible modifications).


Definitely, more detailed event classification can be done.

going for a very fine grained classification, would result in annotations which are difficult to be matched by event extraction systems.


Probably, it is appropriate to consider this as a correct hit.

if the system says that the type is terrorist executing hostages and the annotation is suicide bombing, then the match should not be considered correct.


In the above text, some NCs are interpretable via paraphrasing: protest was carried out during evening, where 'during' defines the temporality of the protest.

some NCs are not explicit: student protest meaning that the 'protest was done by the students' (Agent), London protest meaning 'protest was held in London' (Spatial), monday evening meaning 'evening of monday' (Part-Of), engineering colleges meaning the 'colleges that specialize in engineering course' (Purpose), college funds are the 'funds allocated for the college' (Beneficiary).


Significant improvements are reported in the task from English to Hindi.

there are approaches which use machine learning techniques.


By considering both sorted tables simultaneously, we can select the translation for Arabic word in input test.

each Arabic word (si) in the test corpus is mapped to a single semantic class ci.


One problem with our approach is that the current resources available to us do not contain meanings of all of the idiom phrases.

we believe that with advancement in technology we would be able to do a much better job of obtaining the phrase definitions in the near future.


Thus, it also may help to identify the semantic equivalence between two given texts.

in the STS task, very few systems provide evidence of the contribution of syntactic structure in its overall performance.


Among the three methods, SG seems to integrate well with the bag-of-word approach in which its combinations outperform the baseline on three datasets MSRvid, OnWN, and SMTnews.

none of these settings equals to the baseline in overall result.


Then a special REL tag is used to link the related structures and encode the structural relationships between two sentences.

the dependency relations between words are used to derive an alternative structural representation in which words are linked in a way that words are always at the leaf level.


One limitation is that the metaphor index needs to be derived manually and that manual annotation of metaphors can be an onerous and unreliable process.

we argue that, in the case of readability assessment for autism, a very detailed annotation scheme encoding fine-grained distinctions is unnecessary and that a less detailed approach would be sufficient.


In some cases, an off-line person asks people around a difficult question verbally, then he/she may be recommended by word of mouth to visit the CQA homepages of some potential answer providers.

the links to their homepages are not provided sometimes, then the asker has to search them according to the provided user names.


Some user names are unique, and they can easily access the historical QA records of these potential answer providers.

some are very common and ambiguous, accordingly, many users with the same user name will be displayed.


there is no explicit causal relation between add and fry.

we implicitly know, that without adding the pork pieces, we cannot fry them.


using a grammatical pattern we will discover that simmer causes thickened (through the causal link until).

it will not discover that the sauce has to thicken, in order to add the pork.


The recall increases with decreasing the sentence length, while the false discovery rate (FDR) decreases.

the recall for the grammatical patterns is low for all instructions.


On the other hand, the recall for the grammatical patterns is low for all instructions.

in difference to the time series approach, the grammatical patterns have a high precision.


Another characteristic of textual instructions is that they often omit the direct object.

as the results showed, the usage of objects reduces the generation of false positives.


Special marks placed above or below the letters, namely diacritics, are used to compensate for the absence of short vowels.

these diacritics are rarely used in contemporary writings; yet, it is possible for a native speaker to infer the missing diacritics (Farghaly and Shaalan, 2009).


To overcome the incorrect extraction of entities like this, the observation that it is highly unlikely that an Arabic person"s name will appear beside a foreign person"s name can be utilized.

name lists do not contain information regarding the origin of a name.


Their goal is to label congressional floor-debate speeches as supporting or opposing the discussed topic.

to our approach, where speakers are the nodes, they model speech turns as nodes connected by same label relations.


Measuring how many predicted cells are actual cells in a given table, would be a more explanatory metric for evaluation.

if one token among all the tokens of a cell is wrongly predicted by the relationship classification model as belonging to a different cell, then measuring the correctness of this prediction at a cell-level would be challenging.


The relationship among the tokens of a table is learned by the model based on Neighborhood, Clustering and Alignment features of the tokens.

for a few tokens where the neighborhood features do not have a clear separation with tokens from nearby cells, the model combines the tokens from adjacent cells, producing wrong predictions.


Neural Machine Translation (NMT) has made considerable progress in recent years.

to achieve acceptable translation output, large sets of aligned parallel sentences are required for the training phase.


It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering.


If we take these untransferred instances as incorrect; 19,373 annotations out of 38,780 annotation are true and the accuracy drops to ∼50%.

8,837 of untransferred annotation are not an-Different Arguments # of Occurrence ARG0-ARG1  1,843  ARG1-ARG2  961  ARG2-ARGMEXT  462  ARG1-PREDICATE  255  ARG0-ARG2  229  ARG4-ARGMEXT  226  ARG1-ARGMPNC  220  ARG1-ARGMMNR 186 ARG1-ARGMTMP 160 ARG1-ARGMLOC 148  In the previous method, annotation projection using parallel sentence trees is discussed.


However, 8,837 of untransferred annotation are not an-Different Arguments # of Occurrence ARG0-ARG1  1,843  ARG1-ARG2  961  ARG2-ARGMEXT  462  ARG1-PREDICATE  255  ARG0-ARG2  229  ARG4-ARGMEXT  226  ARG1-ARGMPNC  220  ARG1-ARGMMNR 186 ARG1-ARGMTMP 160 ARG1-ARGMLOC 148  In the previous method, annotation projection using parallel sentence trees is discussed.

finding such a resource in a special format is difficult especially if you are working with a resourcescarce language.


True annotation count is 2,195 among 29,168 annotations tagged manually which yields 7.53%.

transferred false annotation count is only 342.


We can say these semi-space signs segment words into smaller morphemes.

in formal writing and in all Persian normal corpora, this space is neglected frequently and it could make a lot of problems in Persian and Arabic morphological segmentation task.


For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account.

such tools are fairly immature, and, worse, nonexistent for many low resource languages.


It is also used as the spell checker for LibreOffice.

it is not capable of detecting the misspelling of the clitic "de/da" as it does not make a semantic analysis on the sentence.


Note that some words simply end with "de/da" and these suffixes are not due to locative morphemes (i.e., 'ziyade' meaning plentiful).

such cases are few and considered negligible.


The authors focus on historical and cultural changes that are tracked via a quantitative analysis of word frequencies.

they expand their methodology to a "semantic" level by working on named entities extracted from text.


One limit of our approach is the short time frame taken into account since Wikipedia was launched in 2001.

our approach is incremental and the dataset can grow when new Wikipedia dumps are available.


In order to avoid overfitting, NMT models are often trained with L 1 or L 2 regularization, as well as other forms of regularization such as momentum training or dropout (Srivastava et al., 2014;Miceli Barone et al., 2017).

these regularization methods are very general and do not carry any language specific information.


However, these regularization methods are very general and do not carry any language specific information.

it has been shown that transfer learning approaches using out of domain data, such as the European Parliament data 1 , to regularize the learning helps improve the translation quality (Miceli Barone et al., 2017).


We wish to keep only those that generalise well by being broadly applicable.

noise introduced during grammar induction should be removed.


When the target contains only the common slots, we observe a decrease of the success rate with less than 2% with each extra slot added in the source domain, for the model trained with transfer learning.

it is still by 6.6% greater than the success rate of the agent trained with any other prior knowledge.


The outcomes encourage the use of transfer learning with warm start on various cases of overlapping and extending source and target domains.

the optimal selection in terms of hyperparameters of the system, such as the number of epochs or the number of maximum turns in a conversation, need to be determined for each particular scenario.


We observe that our model has ROUGE scores superior to the other heuristic approaches by a margin of approximately 2 points for ROUGE-1 and -L and 1 point for ROUGE-2.

the deep-learning approaches trained explicitly for summarization achieve even higher scores, suggesting that there is more to a good summary than the sentences simply being introductory-like.


All diagnoses are given by ICD-10 7 codes and location names are specified in Bulgarian according to a standard nomenclature.

much information is provided as free text: anamnesis (case history, previous treatments, often family history, risk factors), patient status (summary of patient state, height, weight, body-mass index, blood pressure etc.


Therefore, finding words that end with the Arabic letter Taa' Marbootah such as (Feeling cold), (Feeling sleepy), or (Feeling hungry) in the tweets of a user indicates that the writer is a female.

if the writer is a male, the adjec-tives used in his tweets would not have that letter.


For simplicity, we consider the pairwise adaptation from a single source to single target, and binary (y ∈ {−1, 1}) classification as the target task.

self-adapt can be easily extended to multi-domain and multi-class UDA.


The process is repeated until no further unlabelled instances can be assigned labels with confidence higher than τ .

in our preliminary experiments, we found that this process does not improve the performance in UDA beyond the first iteration.


Many other prepositions such asà part,à l'exception de or excepté, with more or less the same meaning than en dehors de (apart from), would have the same effect on the negation scope.

these prepositions can also play the role of negation by themselves.


In another experiment, we trained the models on one corpus and tested them on the other (Table 5): the models trained on ESSAI are more efficient, and the negation and speculation structures are more stable in the CAS corpus.

even though CAS (speculation) was only trained on 226 examples, the model still shows decent results in scope tokens detection.


This naïve perspective is beginning to change these days with the ever-growing importance of social media documents for text analytics.

there are currently no systematic actions taken to hide personally sensitive information from down-stream applications when dealing with chat, blog, SMS or email raw data.


This approach still allows rare substitutes for common names.

we circumvent the problem of adding ambiguity to the text, if we only have few names starting with 'X' (Stubbs et al.


If we subsume the language-independent categories, because they are randomized and treated similarly in surrogate generation, we get comparable outcomes for all experiments with the PIR system with an F 1 score around 90.00.

language-dependent categories, with the exception of DATEs, which accomplish equivalent results for nearly all experiments, too, consistently perform worse compared to training and testing on the same corpus, especially regarding ORGanizations, CITYs and STREETs.


Readability indexes are interpretable and easy to implement.

the great number of constants tuned specifically for the English language texts, lack of the semantics consideration and tailoring to the US grade level system restrains the number of possible applications.


Most of the mentioned systems focus on one iteration of the extraction process.

more recent approaches, like NELL (Mitchell et al., 2018), attempt to learn continuously from a stream of web data, and increase over time both the amount and the quality of the knowledge discovered.


To ensure timely health information delivery to Arabic users, the patient should be able to tell the bot what symptoms they are experiencing and receive medical advice.

this is not possible with current approaches, since they merely focus on generic approaches.


Around 2 billion people are overweight, but many are ready to change (Van Itallie, 1985).

according to studies (Mann et al., 2007) temporary fixes to old habits makes people to regain their weight.


Representing texts using a sequence of words is useful for exchanging information between humans.

when using neural models, we need to convert the text into numbers that could be used as input into the neural network.


The results show that extractive methods provided weak performance being unable to generate useful summaries.

abstractive models provided much better results, with summaries that were very similar to those produced by humans.


These are referred to as closed cloze questions, since the answer is limited to the alternatives given.

open cloze questions do not provide predefined options, so the student must produce an answer from scratch.


Summarization of a scientific paper can be improved by considering the citations of it, as in (Delort et al., 2003).

to the best of our knowledge no summarization methods have been proposed for the domain of song texts.


By default, lexical development is calculated based on the full list of lexical entries.

this list is organized by word form (types), such that words that are interrelated via inflectional morphology (e.g., cat-cats) are listed as separate entries.


One possibility to check data quality is to have a random subset of the corpus (e.g., 10% of the tokens) be transcribed by more than one transcriber, and calculate inter-transcriber reliability.

such an approach can help detecting problems in a limited part of the corpus.


For words that are not found in the phonetic dictionary, transcription needs to be generated, either manually or with the automatic linguistic model (see next section).

once the additional phonetic word forms are supplied, CPA 4 The tables of rules are generated manually, in principle.